Computer systems and applications generate large amounts of logs to measure and record information, which is vital to protect the systems from malicious attacks and useful for repairing faults, especially with the rapid development of distributed computing. Among various logs, the anomaly log is beneficial for operations and maintenance (O&M) personnel to locate faults and improve efficiency. In this paper, we utilize a large language model, ChatGPT, for the log parser task. We choose the BERT model, a self-supervised framework for log anomaly detection. BERT, an embedded transformer encoder, with a self-attention mechanism can better handle context-dependent tasks such as anomaly log detection. Meanwhile, it is based on the masked language model task and next sentence prediction task in the pretraining period to capture the normal log sequence pattern. The experimental results on two log datasets show that the BERT model combined with an LLM performed better than other classical models such as Deelog and Loganomaly.

Streaming media data have become pervasive in modern commercial systems. To address large-scale data processing in intelligent transportation systems (ITSs), recent research has focused on deep neural network–based (DNN-based) approaches to improve the performance of cross-modal hashing retrieval (CMHR) systems. However, due to their high dimensionality and network depth, DNN-based CMHR systems inherently suffer from vulnerabilities to malicious adversarial examples (AEs). This paper investigates the robustness of CMHR-based ITS systems against AEs. Prior work typically formulates AE generation as an optimization-driven, iterative process, whose high computational cost and slow generation speed limit research efficiency. To overcome these limitations, we propose a parallel cross-modal relevance-guided generative network (ReGeNet) that captures the semantic characteristics of the target deep hashing model. During training, we design a relevance-guided adversarial generative framework to efficiently learn AE generation. During inference, the well-trained parallel adversarial generator produces adversarial cross-modal data with effectiveness comparable to that of iterative methods. Experimental results demonstrate that ReGeNet can generate AEs significantly faster while achieving competitive attack performance relative to iterative-based approaches.